Yogesh Chatur
About Yogesh Chatur
Yogesh Chatur is a Python Developer with a background in data science and business analytics. He has experience working at The Sparks Foundation, Chegg India, and currently at Meticulous Research®, where he develops machine learning solutions.
Work at Meticulous Research®
Yogesh Chatur currently serves as a Python Developer at Meticulous Research®, a position he has held since 2023. In this role, he focuses on developing end-to-end machine learning solutions. His responsibilities include data preprocessing, feature engineering, model training, deployment, and optimization. He works on-site, collaborating with cross-functional teams to address various business challenges through innovative solutions.
Previous Experience at Chegg India
Prior to his current role, Yogesh Chatur worked at Chegg India as a Subject Matter Expert from 2021 to 2023. During his two-year tenure, he contributed to educational content and provided expertise in his subject area, enhancing the learning experience for students. His role involved engaging with students and addressing their queries, showcasing his knowledge and communication skills.
Internship at The Sparks Foundation
In 2022, Yogesh Chatur completed a one-month internship at The Sparks Foundation, focusing on Data Science and Business Analytics. This experience allowed him to apply his academic knowledge in a practical setting, gaining insights into data-driven decision-making and analytics methodologies.
Education and Expertise in Data Science
Yogesh Chatur has a strong educational background in statistics and data science. He achieved a Master of Science (MSc) in Statistics from KTHM College Nashik from 2019 to 2021. He also completed a Bachelor of Science (BSc) in Statistics at the same institution from 2016 to 2019. Additionally, he studied Data Science at Imarticus Learning, where he completed a Post Graduate Program in Data Analytics and Machine Learning in 2022.
Technical Skills and Proficiencies
Yogesh Chatur is proficient in various Python libraries and frameworks, including scikit-learn, TensorFlow, and Django. His expertise in these tools supports his work in machine learning projects, enabling him to develop effective algorithms and applications. His technical skills are complemented by his ability to collaborate with cross-functional teams to deliver solutions that meet business needs.